Singuron

The first atom of singularity.

What we are

We build the reliability layer that keeps an AI agent's identity stable. That means three things: a formal impossibility theorem, a model-agnostic way to enforce it, and an open adversarial benchmark to measure it.

Long-horizon agents get pushed off course by social pressure, authority override, and memory poisoning. Our middleware drops in at write time to hold the line, and it works on closed APIs and frozen models without ever touching their internals.

Vision

We want AI systems that keep a stable, trustworthy sense of self even when they run on their own for a long time. That is the foundation a safe, long-lived agent needs to grow while staying itself and staying under human control.

  • We never claim phenomenal consciousness in any artifact or pitch.
  • We use only functional language and behavioral monitoring.
  • We will never claim superintelligence as a deliverable.

Foundation

Why this? What is the need?

AI agents now run for a long time. They field thousands of messages and rewrite their own memory, and they have to keep learning from all of it to stay useful. The trouble is that the same update channel is also how they get attacked. Three failure modes are well documented, and at the deployment layer nobody has fixed them:

Persona drift

Over long interactions, agents quietly lose their stated values and expertise. Bigger models tend to drift the most.

Social capture

Highly capable models are highly vulnerable to persuasion, conformity, and authority-framed jailbreaks.

Memory poisoning

A small number of injected memories can durably rewrite an agent's behavior.

For enterprise agents touching healthcare, finance, or security, any one of these is a release-blocker. The field needs selective rigidity: the joint ability to resist identity-violating pressure while accepting identity-preserving corrections.

The current scenario

Long-horizon agents and memory systems are shipping everywhere. Labs like Anthropic have confirmed persona drift and built white-box fixes such as activation capping, but those only work if you can reach inside the model. That leaves an obvious gap: a model-agnostic standard that evaluates and enforces behavior at write time. Filling that gap is what Singuron does.

On AGI, ASI, and the singularity

Singuron grew out of an earlier project, CORE AI, which once chased consciousness and artificial superintelligence. We retired that whole framing on purpose, because the science of 2026 moved against it.

Consciousness claims rejected

Reviewers flagged any "we build consciousness" claim as contested. Recent work undercuts functionalist theories, with some analyses putting the integrated information of large language models near Φ ≈ 0. The honest position today is methodological agnosticism, and that is ours.

Superintelligence and singularity risks

Claiming ASI as a deliverable is unfalsifiable and contradicts the safety consensus. Autonomous capability expansion and automated AI research are flagged as dangerous by every frontier safety framework, given the risk of insufficient oversight.

So we dropped the AGI, ASI, and singularity framing entirely. What we stand on instead is bounded self-improvement. An agent can refine its own policies and scaffolding, but never its base weights on its own, and only when each change clears an adversarial identity benchmark and stays under human control.